The type of environment, as well as the presence or absence of an LOS component, are two factors that influence the error of ML models. ML propagation models in urban scenarios exhibit lower accuracy than in rural [1]} or semi-urban [2]}, [3]} environments. A similar finding was reported in [4]}. The authors noted a slight increase in error when they inserted more buildings in their RT-generated urban grid, i.e. when they made the urban environment even more complex. Moreover, ML models for NLOS scenarios are also less accurate than LOS ones [5]}, [6]}, [7]}, [8]}. Similar results were reported in [9]}, where the authors trained and tested their network using two different routes in an urban environment and ascertained that the route that maintained better LOS conditions exhibited smaller prediction errors. Also in [10]}, the largest errors corresponded to receiving points for which LOS was obstructed by several tall buildings. For such cases, the authors found that local (around the receiver) information could improve accuracy. In addition to that, increasing the height of the transmitter had the same effect, since by doing so, the signal strength at the same receiving points increased (some NLOS cases also changed into LOS ones). Similar findings with respect to the transmitter antenna height were reported in [3]}.
